CompromisedAccountDetection

This application was developed in the context of the Social Media Mining seminar at Hasso-Plattner Institute. It aims to detect tweets in the timeline of a given user, which are suspicious in order to tell if the account was compromised.

Setup

Docker

The app can be run using docker. The following steps are necessary:

1. Install docker. See https://www.docker.com/products/docker#/ for details.
2. Clone the repository.
3. Create the twitter API credentials file as described below.
4. Copy the twitter dataset into data/tweets.csv. It can be created using the CLI interface (see CLI Reference).
5. Go into the project directory, build the image and run it.

cd /SMM-CompromisedAccountDetection
docker build -t smm-compromised-account-detection .
docker run -d -p 5000:5000 smm-compromised-account-detection

Mac & Linux

The app is available at http://localhost:5000.

Windows

Execute the following command to determine the IP of the Docker VM (DOCKER_VM_IP).

docker-machine ip default  # the machine could be named different from 'default' !

The app is available at http://DOCKER_VM_IP:5000.

Native

Python

Python 3 is required to run the app.

Requirements

You have to install the required packages using pip and the necessary NLTK data packages:

pip install -r requirements.txt
python -m nltk.downloader punkt

Twitter API credentials

For both options, you have to create a file twitter_credentials.json based on the template containing your Twitter API credentials.

Running

Web App

The web app takes a twitter user id as input, crawls the tweets of the user and shows those, which are detected as suspicious.

For demo purposes the HTTP query parameter demo=1 can be set to randomly insert some external tweets into the timeline of the user. These tweets should be detected by the app.

The app needs a dataset containing tweets that can be used as negative samples and for demo mode. It can be created by using our crawling command-line tool (see CLI Reference).

# Starts the web app with default parameters
python app.py

The app can be configured using the following command line arguments:

Flag	Name	Description	Default
-H	--host	The hostname of the app.	0.0.0.0
-P	--port	The port for the app.	5000
-s	--data-source	Type of the status update dataset. Possible values are fth, mp and twitter.	twitter
-p	--dataset-path	Path of the status update dataset.	data/tweets.csv
-c	--classifier	The classifier to use. Possible values are decision_tree, one_class_svm, isolation_forest and perceptron.	decision_tree

Command Line Interface

The app provides a command line interface for crawling a dataset, tuning hyperparameters, and evaluation. Have a look at the CLI Reference.